ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

Abstract. The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Meteo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

[1]  J. Mahfouf,et al.  The ISBA land surface parameterisation scheme , 1996 .

[2]  G. Kramm,et al.  Determination of HNO3 dry deposition by modified Bowen ratio and aerodynamic profile techniques , 1993 .

[3]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[4]  Eric C. Brown de Colstoun,et al.  The International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[5]  Catherine Mering,et al.  Ecosystem mapping at the African continent scale using a hybrid clustering approach based on 1-km resolution multi-annual data from SPOT-VEGETATION . , 2011 .

[6]  E. A. Clark,et al.  Plant Functional Types. Their Relevance to Ecosystem Properties and Global Change , 1998 .

[7]  Eea Sustainable use and management of natural resources , 2005 .

[8]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[9]  C. C. Lautenbacher,et al.  The global earth observation system of systems (GEOSS) , 2005, 2005 IEEE International Symposium on Mass Storage Systems and Technology.

[10]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[11]  P. Shi,et al.  Northern hemispheric NDVI variations associated with large-scale climate indices in spring , 2003 .

[12]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[13]  Rasmus Fensholt,et al.  MODIS leaf area index products: from validation to algorithm improvement , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[14]  S. Fritz,et al.  A new land‐cover map of Africa for the year 2000 , 2004 .

[15]  A. Molod,et al.  A global assessment of the mosaic approach to modeling land surface heterogeneity , 2002 .

[16]  A. D. Gregorio,et al.  Land Cover Classification System (LCCS): Classification Concepts and User Manual , 2000 .

[17]  Steffen Fritz,et al.  Cropland for sub‐Saharan Africa: A synergistic approach using five land cover data sets , 2011 .

[18]  K. Oleson,et al.  A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics , 2003 .

[19]  R. Knutti,et al.  Weather and Climate , 2008 .

[20]  R. DeFries,et al.  Global distribution of C3 and C4 vegetation: Carbon cycle implications , 2003 .

[21]  Peter R. J. North,et al.  Forest ecosystem chlorophyll content: Implications for remotely sensed estimates of net primary productivity , 2003 .

[22]  Steffen Fritz,et al.  Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover , 2009, Remote. Sens..

[23]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[24]  Masson,et al.  The SURFEXv7.2 externalized platform for the simulation of Earth surface variables and fluxes. , 2012 .

[25]  O. Hagollea,et al.  Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images , 2009 .

[26]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[27]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  A. Henderson‐sellers,et al.  A global archive of land cover and soils data for use in general circulation climate models , 1985 .

[29]  F. Baret,et al.  LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products , 2007 .

[30]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[31]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[32]  Roger A. Pielke,et al.  A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology , 1989 .

[33]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[34]  A. Dolman,et al.  Atmospheric CO2 modeling at the regional scale: an intercomparison of 5 meso-scale atmospheric models. , 2007 .

[35]  R. Cowling,et al.  One Hundred Questions of Importance to the Conservation of Global Biological Diversity , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[36]  A. Tatem,et al.  Food and Agriculture Organisation of the United Nations , 2009 .

[37]  Jean-Louis Roujean,et al.  ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1 km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project , 2010 .

[38]  Christelle Vancutsem,et al.  GLOBCOVER : A 300 M Global Land Cover Product for 2005 using ENVISAT MERIS time series , 2006 .

[39]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[40]  A. Belward,et al.  GLC 2000 : a new approach to global land cover mapping from Earth observation data , 2005 .

[41]  Kyung-Soo Han,et al.  A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data , 2004 .

[42]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[43]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[44]  Catherine Ottlé,et al.  The AMMA Land Surface Model Intercomparison Project (ALMIP) , 2007 .

[45]  H. Mooney,et al.  Human Domination of Earth’s Ecosystems , 1997, Renewable Energy.

[46]  R. Lacaze,et al.  A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models , 2003 .

[47]  Olivier Hagolle,et al.  Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images , 2005 .

[48]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[49]  J. Roujean,et al.  A New Characterization of the Land Surface Heterogeneity over Africa for Use in Land Surface Models , 2011 .

[50]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[51]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[52]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .

[53]  B. Duchemin,et al.  VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products , 2004 .

[54]  L. Telesca,et al.  Fire‐induced variability in satellite SPOT‐VGT NDVI vegetational data , 2006 .

[55]  Jean-Christophe Calvet,et al.  Atmospheric CO2 modeling at the regional scale: Application to the CarboEurope Regional Experiment , 2007 .

[56]  G. Donald,et al.  Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series , 2003 .